Device to enhance and present medical image using corrective mechanism

ABSTRACT

A device to enhance and present a medical image using a corrective mechanism is described. An image analysis application executed by the device captures a digital copy of the medical image displayed on a display device. A flawed photography effect associated with the digital copy is identified by processing the digital copy. Next, the digital copy is enhanced based on the flawed photography effect. Furthermore, the enhanced digital copy can be processed with an artificial intelligence mechanism to generate an annotation. The annotation is associated with a cancer identification. In addition, the enhanced digital copy and the annotation are displayed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation Application relating to and claimingthe benefit of commonly-owned, co-pending PCT International ApplicationNo. PCT/US2019/034819, filed May 31, 2019, which claims priority to andthe benefit of U.S. patent application Ser. No. 16/190,598, filed Nov.14, 2018, issued as U.S. Pat. No. 10,453,570 on Oct. 22, 2019, each ofwhich claims priority to and the benefit of U.S. Provisional ApplicationSer. No. 62/680,230 filed on Jun. 4, 2018 and to U.S. ProvisionalApplication Ser. No. 62/690,008 filed on Jun. 26, 2018, the contents ofeach of the foregoing are herein incorporated by reference in itsentirety.

FIELD OF THE EMBODIMENTS

The field of the embodiments relate to a device to enhance and present amedical image using a corrective mechanism. The corrective mechanism mayremove a flawed digital photography effect from a digital copy of themedical image prior to processing the digital copy to determine anannotation.

BACKGROUND OF THE EMBODIMENTS

Information exchanges have changed processes associated with work andpersonal environments. Automation and improvements in processes haveexpanded the scope of capabilities offered for personal and businessdata consumption. With the development of faster and smallerelectronics, a variety of mobile devices have integrated into dailylives. A modern mobile device includes components to provide variety ofservices such as communication, display, imaging, voice, and/or datacapture, among others. Abilities of the modern mobile device jumpexponentially when networked to other resources that provide previouslyunimagined number of services associated with medical imaging.

Commercial Medical Imaging systems such as ultrasound, x-ray,mammography, computed tomography (CT), magnetic resonance imaging (MRI),positron emission tomography (PET) use proprietary software to protectthe manufacturers' intellectual property and regulation compliantstorage and communication protocols to ensure patient privacy. Imagingsystems are used for detection and diagnosis of medical conditions suchas cancer, cardiovascular disease and other diseases of different bodyparts including but not limited to breast, lungs, musculoskeletal,thyroid, kidney, liver, prostate and other body parts. Third partysoftware applications developers face potential challenges associatedwith many different software platforms which support the numerousvendors' hardware medical imaging devices.

SUMMARY OF THE EMBODIMENTS

The present invention and its embodiments relate to a device to enhanceand present a medical image using a corrective mechanism. The device maybe configured to capture a digital copy of the medical image displayedon a display device. Next, a flawed photography effect associated withthe digital copy may be identified by processing the digital copy with acomputer analysis and correction module (CACM). The digital copy may beenhanced with the CACM based on the flawed photography effect. Theenhanced digital copy may be directly displayed for diagnosis orprocessed with an additional artificial intelligence mechanism such asany a computer assisted diagnosis (CADx) or a computer assisteddetection (CADe) system that is available to generate an annotationassociated with the enhanced digital copy. The annotation reported maybe associated with a cancer identification or other disease state . Theannotation may be numeric such as the probability of malignancy. Theannotation might be pictorial such as the position of the images' scorewithin a histogram of scores of images that were benign to malignant.The annotation can also be in the form of images such as similar imagesthat were found to contain cancer or be benign under biopsy. Inaddition, both the enhanced digital copy and the annotation may bedisplayed.

In another embodiment of the present invention, a mobile device forenhancing and presenting a medical image using a corrective mechanism isdescribed. The mobile device may include a display component configuredto accept an input and display an output associated with an imageanalysis application. A camera component may be configured to capture adigital copy of the medical image in relation to the image analysisapplication. A memory may be configured to store instructions associatedwith the image analysis application. A processor may be coupled to thedisplay component, the camera component, and the memory. The processormay execute the instructions associated with the image analysisapplication. The image analysis application may include a computerassisted diagnosis module (CACM). The CACM may be configured to receivea first digital copy of the medical image from the camera component. Thecamera component may be configured to capture the first digital copy ofthe medical image displayed on a display device. Next, a flawedphotography effect associated with the first digital copy may beidentified by processing the first digital copy. The first digital copymay be enhanced based on the flawed photography effect. Furthermore, thefirst digital copy may be processed with an artificial intelligencemechanism to generate an annotation. The annotation may include asuspicious label, a not suspicious label, or a follow-up labelassociated with a cancer identification or identification of anotherdisease state. The first digital copy and the annotation may bedisplayed on the display component.

In yet another embodiment of the present invention, a method ofenhancing and presenting a medical image using a corrective mechanism isdescribed. The method includes receiving a digital copy of the medicalimage from an image source. Next, a flawed photography effect associatedwith the digital copy may be identified by processing the digital copy.The digital copy may be enhanced based on the flawed photography effect.Furthermore, the digital copy may be processed with an artificialintelligence mechanism to generate an annotation. The annotation mayinclude a suspicious label, a not suspicious label, or a follow-up labelassociated with a cancer identification or identification of anotherdisease state. The digital copy and the annotation may next be providedfor a presentation.

It is an object of the embodiments of the present invention to enhance adigital copy of a medical image captured by a camera of a mobile device.

It is an object of the embodiments of the present invention to annotatethe digital copy associated with a cancer identification.

It is an object of the embodiments of the present invention to detect aflawed photography effect associated with the digital copy.

It is an object of the embodiments of the present invention to removethe flawed photography effect.

It is an object of the embodiments of the present invention to presentthe enhanced digital copy along with the annotation to a user.

These and other features, aspects and advantages of the presentinvention will become better understood with reference to the followingdrawings, description and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a conceptual diagram illustrating examples of enhancing andpresenting a medical image using a corrective mechanism, according to anembodiment of the invention.

FIG. 2 shows a display diagram illustrating interactions betweencomponents of a device that enhances and presents a medical image usinga corrective mechanism, according to an embodiment of the invention.

FIG. 3 shows a display diagram illustrating a corrective mechanism usedto enhance and present a medical image, according to an embodiment ofthe invention.

FIG. 4 shows a display diagram illustrating an example of a userexperience enhancing and presenting a medical image using a correctivemechanism, according to an embodiment of the invention.

FIG. 5 is a block diagram of an example computing device, which may beused to enhance and present a medical image using a correctivemechanism.

FIG. 6 is a logic flow diagram illustrating a process for enhancing andpresenting a medical image using a corrective mechanism, according to anembodiment of the invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The preferred embodiments of the present invention will now be describedwith reference to the drawings. Identical elements in the variousfigures are identified with the same reference numerals.

Reference will now be made in detail to each embodiment of the presentinvention. Such embodiments are provided by way of explanation of thepresent invention, which is not intended to be limited thereto. In fact,those of ordinary skill in the art may appreciate upon reading thepresent specification and viewing the present drawings that variousmodifications and variations may be made thereto.

FIG. 1 shows a conceptual diagram illustrating examples of enhancing andpresenting a medical image using a corrective mechanism. In an examplescenario, a mobile device 104 may execute (or provide) an image analysisapplication 106. The mobile device 104 may include a physical computingdevice hosting and/or providing features associated with a clientapplication (such as the image analysis application 106). The mobiledevice 104 may include and/or is part of a smart phone, a tablet baseddevice, and/or a laptop computer, among others. The mobile device 104may also be a node of a network. The network may also include othernodes such as a display device 112, among others. The network mayconnect nodes with wired and wireless infrastructure.

The mobile device 104 may execute the image analysis application 106.The image analysis application 106 may capture a digital copy 108 of amedical image 111 displayed on a display device 112. An example of themedical image 111 may include an ultrasound image, a x-ray image, amagnetic resonance imaging (MRI) scan, a computed tomography (CT) scan,and/or a positron emission tomography (PET) scan, among others. Thedisplay device 112 may include a monitor associated with a diagnosticsystem that captures, manages, and/or presents the medical image 111 toa user 102 such as a doctor, a nurse, a technician, a patient, and/or anadministrator, among others.

The medical image 111 and the digital copy 108 may include a region ofinterest (ROI) 109. The ROI 109 may represent a malignant or a benigntumor. Alternatively, the ROI 109 may represent another structureassociated with an organ and/or other part of a patient captured in themedical image 111. Alternately the operator can select a region ofinterest within the digital image or alternatively a computer assisteddetection (CADe) module can select the ROI automatically.

The image analysis application 106 may next identify a flawedphotography effect associated with the digital copy 108 by processingthe digital copy 108 with a computer analysis and correction module(CACM). The CACM may analyze and enhance the digital copy 108 andgenerate an enhanced version of the digital copy 108. An artificialintelligence (AI) mechanism such as the CACM, a computer assisteddiagnosis (CADx), and/or the CADe may be used to process, analyze, anddesignate the digital copy 108 with an annotation. The AI may be acomponent of the image analysis application 106. Alternatively, the AIsystem, such as the CACM, may be a service provided by an externalentity such as another application. The flawed photography effect mayinclude number of errors associated with capture and/or processing ofthe medical image 111 and/or the digital copy 108.

The image analysis application 106 may enhance the digital copy 108based on the flawed photography effect. For example, the image analysisapplication 106 may remove the flawed photograph effect such as a glareeffect and/or a reflection effect (associated with use of a flash and/orlighting in the vicinity of the medical image 111). The computeranalysis might find one or more faults or no faults. If no faults arefound the image is passed on to the next step of the diagnostic process.If one or more faults are found by the computer analysis component thosefaults are corrected by the correction component of the CACM. Next, thedigital copy 108 may be processed with the AI mechanism to generate anannotation. The AI mechanism may include a deep learning, a machinevision, and/or a machine learning mechanism, among others basedmechanism to process and classify the digital copy 108 and the ROI 109.The annotation may be associated with a cancer identification. Forexample, the annotation may include a suspicious, a not suspicious,and/or a follow up label. The image analysis application 106 may nextdisplay the digital copy 108 and the annotation to the user 102.

Previous example(s) to enhance and present a medical image are notprovided in a limiting sense. Alternatively, the image analysisapplication 106 may perform operations associated with enhancing andpresenting the digital copy 108 as a desktop application, a workstationapplication, and/or a server application, among others. The imageanalysis application 106 may also be a client interface of a serverbased application.

The user 102 may interact with the image analysis application 106 with akeyboard based input, a mouse based input, a voice based input, a penbased input, and a gesture based input, among others. The gesture basedinput may include one or more touch based actions such as a touchaction, a swipe action, and a combination of each, among others. Theuser 102 may also interact with the CACM by use of an augmented realityheadset which provides an annotated overlay on top of the screen of themedical image display.

While the example system in FIG. 1 has been described with specificcomponents including the mobile device 104, the image analysisapplication 106, embodiments are not limited to these components orsystem configurations and can be implemented with other systemconfiguration employing fewer or additional components.

FIG. 2 shows a display diagram illustrating interactions betweencomponents of a device that enhances and presents the medical imageusing a corrective mechanism. In an example scenario, the mobile device104 may include components such as a camera 220 and a polarizer 222. Thepolarizer 222 may be attached or integrated to a lens of the camera 220.The polarizer 222 may be rotated to pass only light polarized in adirection perpendicular to a reflected light. As such, the polarizer 222may absorb a significant proportion of the reflected light. As a result,the polarizer 222 may absorb a glare effect and/or a reflection effectthat results from capturing the digital copy 108 from a display devicedisplaying the medical image.

In an example scenario, the image analysis application 106 may processthe digital copy 108 to enhance and annotate the digital copy 108 of themedical image. The digital copy 108 may include the ROI 109. The ROI 109may include a lesion or another structure associated with the organ orother body component that is scanned by the medical image.

The image analysis application 106 may process the digital copy 108 withthe CACM 224 to determine a flawed photography effect 230. The flawedphotography effect 230 may arise from a number errors associated withcapturing the digital copy 108 from the displayed medical image.Furthermore, the flawed photography effect 230 may also be a result oferror(s) introduced during the initial capture of the medical image.Identification of the flawed photography effect 230 and an enhancementprocess to correct error(s) associated with the flawed photograph effect230 may aid or substantially impact a determination of a correctannotation of the digital copy 108. A likelihood of determining thecorrect annotation may also improve in response to identification of theflawed photography effect 230 and enhancement of the digital copy 108based on the flawed photography effect 230.

In an example scenario, an analysis engine 226 of the CACM 224 mayprocess the digital copy 108 to identify the flawed photography effect230. The analysis engine 226 may analyze non-image informationassociated with the digital copy 108 to identify the flawed digitaleffect 230. The non-image information may include a flash information, afocal length, a shutter speed, a camera model information, an aperturesetting, and/or a capture date and time information, among others. Forexample, the analysis engine 226 may determine the focal length of thecamera in an incorrect configuration as the flawed photography effect230. In another example scenario, the shutter speed may be determined tobe not optimal in relation to ambient lighting.

In response, an enhancement engine 228 (of the CACM 224) may enhance thedigital copy 108 to remove and/or alleviate the flawed digital effect230. For example, the enhancement engine 228 may lighten or darken thedigital copy 108 to alleviate an underexposure or an overexposure(determined as the flawed digital effect 230) to produce an enhanceddigital copy 232.

The analysis engine 226 may also analyze image characteristic(s) of thedigital copy 108 to identify the flawed photography effect 230. Theimage characteristic(s) may include an orientation, image blur, ared-eye detection, a blur, a color balance, an exposure, and/or a noiseinformation, among others associated with the digital copy 108. Uponidentifying the flawed photography effect, the enhancement engine 228may process the digital copy 108 to remove the flawed photography effect230. For example, the enhancement engine 228 may remove the flawedphotography effect 230 by re-orienting the digital copy 108, removing ared-eye effect, and/or adjusting a color of the digital copy 108, amongother operations to produce the enhanced digital copy 232.

Next, the CACM diagnosis module 224 may further process the enhanceddigital copy 232 with an AI mechanism to determine an annotation 234 TheAI mechanism my include a deep learning, a neural network, a machinevision, and/or a machine learning mechanism, among others. The AImechanism may identify the ROI 109 and classify the ROI 109 with theannotation 234. The annotation 234 may include a cancer identification.The annotation 234 may include a suspicious label, a not suspiciouslabel, and/or a follow-up label, among others.

FIG. 3 shows a display diagram illustrating enhancing and presenting amedical image using a corrective mechanism. The image analysisapplication 106 (executed by the mobile device 104) may process/analyzethe digital copy 108 with the CACM 224 to identify the flawedphotography effect. In an example scenario, the CACM 224 may process thenon-image information 336 and/or the image characteristics information338 obtained by computer vision analysis tools to identify the flawedphotography effect, as previously described.

Furthermore, an orientation information 340 associated with the digitalcopy 108 may be processed to identify issues associated with anorientation of the digital copy 108. The CACM 224 may enhance thedigital copy 108 by re-orienting the digital copy 108 to change aperspective associated with the ROI 109. A change in the perspectiveassociated with the ROI 109 may increase a probability of the CACM 224to determine the annotation 234.

The CACM 224 may identify the ROI 109 using the AI mechanism. A shapeand/or a structure, among other attributes associated with the ROI 109may be used to determine the annotation 234. The flawed photographyeffect may also be identified by processing a shape and/or a structureof the ROI 109. The flawed photography effect may be corrected toenhance the ROI 109 with a clearer shape and/or structure. Furthermore,the user may be allowed to provide an input that selects a region of thedigital copy 108 as the ROI 109.

In another example scenario, the CACM 224 may identify a glare 344effect and/or a reflection 346 effect. The glare 344 effect may obscurethe digital copy 108 by reducing brightness and/or contrast of thedigital copy 108 due to a hotspot caused by a light source (at the timeof capture). The CACM 224 may reduce or remove the glare 344 effect toenhance the digital copy 108.

The reflection 346 effect may introduce a reflection of an externalentity to the digital copy 108 at the time of capture. The CACM 224 mayidentify the reflection 346 of the external entity as the flawedphotography effect and remove the reflection 346 from the digital copy108. The CACM 224 may also identify a moire pattern 348 as the flaweddigital effect within the digital copy 108. The moire pattern 348 mayinclude round stripe(s) within the digital copy 108. The CACM 224 mayremove the moire pattern to produce the enhanced digital copy 232. TheCACM 224 may further identify a color imbalance 350 as the flaweddigital effect within the digital copy 108. The color imbalance 350 mayproduce a rainbow or a rippling of color on the digital copy 108. TheCACM 224 may remove the color imbalance to produce the enhanced digitalcopy 232.

The CACM 224 may identify a geometric distortion 352 as the flaweddigital effect within the digital copy 108. The geometric distortion 352may be caused by an off axis capture of the digital copy 108. The CACM224 may re-orient the digital copy 108 to correct the geometricdistortion 352.

The CACM 224 may identify a motion blur 354 as the flawed photographyeffect within the digital copy 108. The motion blur 354 may be caused bya movement of an organ and/or the medical imaging device while capturingthe medical image. In addition, motion blur 354 may be caused by patientmovement during the image acquisition process. Alternatively, the motionblur 354 may be caused by shaking of a hand of the user capturing thedigital copy 108. The CACM 224 may compensate for the motion blur 354within the digital copy 108 and produce the enhanced digital copy 232.

The CACM 224 may also identify a noise related issue and/or a lightexposure 356 issue as the flawed digital effect within the digital copy108. The CACM 224 may enhance the digital copy 108 to remove and reducethe noise and alleviate the light exposure 356 issue to produce theenhanced digital copy 232.

Furthermore, the CACM 224 may provide a color map 358 of the ROI 109within the enhanced digital copy 232 as the annotation 234 and/or aspart of the annotation 234. The color map 358 may describe probabilityof a malignancy (of cancer) associated with the ROI 109 and/orsection(s) of the ROI 109.

In another example scenario, the CACM 224 may process another digitalcopy of the medical image in addition to the digital copy 108.Processing of the other digital copy may allow the CACM 224 clarify theROI 109 by comparing and contrasting the differences between the twocopies. The CACM 224 may also process additional digital copies in theanalysis procedure to refine the ROI 109.

FIG. 4 shows a display diagram illustrating an example of a userexperience enhancing and presenting a medical image using a correctivemechanism. In an example scenario, the image analysis application 106(executed by the mobile device 104) may display an enhanced digital copy232 of the medical image. The enhanced digital copy 232 may bedesignated with an annotation 234. The annotation 234 may include asuspicious 460 label. The suspicious 460 label may describe a likelihoodof malignancy (of cancer) associated with the ROI 109 in the enhanceddigital copy 232. Furthermore, the annotation 234 may include a notsuspicious 462 label. The not suspicious 462 label may describe a lackof a malignancy (of cancer) associated with the ROI 109. The annotation234 may also include a follow up 464 label. The follow up 464 label maydescribe a necessity to have the enhanced digital copy 232 re-evaluatedby a doctor and/or other practitioner to distinguish malignancy relatedinformation from the enhanced digital copy 232.

The example scenarios and schemas in FIGS. 1 through 4 are shown withspecific components, data types, and configurations. Embodiments are notlimited to systems according to these example configurations. A deviceto enhance and present a medical image using a corrective mechanism maybe implemented in configurations employing fewer or additionalcomponents in applications and user interfaces. Furthermore, the exampleschema and components shown in FIGS. 1 through 4 and their subcomponentsmay be implemented in a similar manner with other values using theprinciples described herein.

FIG. 5 is a block diagram of an example computing device, which may beused to enhance and provide a medical image using a correctivemechanism, according to embodiments.

For example, computing device 500 may be used as a server, desktopcomputer, portable computer, smart phone, special purpose computer, orsimilar device. In a basic configuration 502, the computing device 500may include one or more processors 504 and a system memory 506. A memorybus 508 may be used for communication between the processor 504 and thesystem memory 506. The basic configuration 502 may be illustrated inFIG. 5 by those components within the inner dashed line.

Depending on the desired configuration, the processor 504 may be of anytype, including but not limited to a microprocessor (μP), amicrocontroller (μC), a digital signal processor (DSP), or anycombination thereof. The processor 504 may include one more levels ofcaching, such as a level cache memory 512, one or more processor cores514, and registers 516. The example processor cores 514 may (each)include an arithmetic logic unit (ALU), a floating-point unit (FPU), adigital signal processing core (DSP Core), a graphics processing unit(GPU), or any combination thereof. An example memory controller 518 mayalso be used with the processor 504, or in some implementations, thememory controller 518 may be an internal part of the processor 504.

Depending on the desired configuration, the system memory 506 may be ofany type including but not limited to volatile memory (such as RAM),non-volatile memory (such as ROM, flash memory, etc.), or anycombination thereof. The system memory 506 may include an operatingsystem 520, the image analysis application 106, and a program data 524.The image analysis application 106 may include components such as theCACM 224. The CACM 224 may execute the instructions and processesassociated with the image analysis application 106. In an examplescenario, the CACM 224 may capture a digital copy of the medical imagedisplayed on a display device. A flawed photography effect associatedwith the digital copy may be identified by processing the digital copy.Next, the digital copy may be enhanced based on the flawed photographyeffect. Furthermore, the enhanced digital copy may be processed with anartificial intelligence mechanism to generate an annotation. Theannotation may be associated with a cancer identification. In addition,the enhanced digital copy and the annotation may be displayed.

Input to and output out of the image analysis application 106 may becaptured and displayed through a display component that may beintegrated to the computing device 500. The display component mayinclude a display screen, and/or a display monitor, among others thatmay capture an input through a touch/gesture based component such as adigitizer. The program data 524 may also include, among other data, thedigital copy 108, or the like, as described herein. The digital copy 108may be enhanced and presented with an annotation associated with acancer identification, among other things.

The computing device 500 may have additional features or functionality,and additional interfaces to facilitate communications between the basicconfiguration 502 and any desired devices and interfaces. For example, abus/interface controller 530 may be used to facilitate communicationsbetween the basic configuration 502 and one or more data storage devices532 via a storage interface bus 534. The data storage devices 532 may beone or more removable storage devices 536, one or more non-removablestorage devices 538, or a combination thereof. Examples of the removablestorage and the non-removable storage devices may include magnetic diskdevices, such as flexible disk drives and hard-disk drives (HDDs),optical disk drives such as compact disk (CD) drives or digitalversatile disk (DVD) drives, solid state drives (SSDs), and tape drives,to name a few. Example computer storage media may include volatile andnonvolatile, removable, and non-removable media implemented in anymethod or technology for storage of information, such ascomputer-readable instructions, data structures, program modules, orother data.

The system memory 506, the removable storage devices 536 and thenon-removable storage devices 538 are examples of computer storagemedia. Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVDs), solid state drives, or other optical storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which may be used to storethe desired information and which may be accessed by the computingdevice 500. Any such computer storage media may be part of the computingdevice 500.

The computing device 500 may also include an interface bus 540 forfacilitating communication from various interface devices (for example,one or more output devices 542, one or more peripheral interfaces 544,and one or more communication devices 566) to the basic configuration502 via the bus/interface controller 530. Some of the example outputdevices 542 include a graphics processing unit 548 and an audioprocessing unit 550, which may be configured to communicate to variousexternal devices such as a display or speakers via one or more A/V ports552. One or more example peripheral interfaces 544 may include a serialinterface controller 554 or a parallel interface controller 556, whichmay be configured to communicate with external devices such as inputdevices (for example, keyboard, mouse, pen, voice input device, touchinput device, etc.) or other peripheral devices (for example, printer,scanner, etc.) via one or more I/O ports 558. An example of thecommunication device(s) 566 includes a network controller 560, which maybe arranged to facilitate communications with one or more othercomputing devices 562 over a network communication link via one or morecommunication ports 564. The one or more other computing devices 562 mayinclude servers, computing devices, and comparable devices.

The network communication link may be one example of a communicationmedia. Communication media may typically be embodied by computerreadable instructions, data structures, program modules, or other datain a modulated data signal, such as a carrier wave or other transportmechanism, and may include any information delivery media. A “modulateddata signal” may be a signal that has one or more of its characteristicsset or changed in such a manner as to encode information in the signal.By way of example, and not limitation, communication media may includewired media such as a wired network or direct-wired connection, andwireless media such as acoustic, radio frequency (RF), microwave,infrared (IR) and other wireless media. The term computer readable mediaas used herein may include both storage media and communication media.

The computing device 500 may be implemented as a part of a specializedserver, mainframe, or similar computer, which includes any of the abovefunctions. The computing device 500 may also be implemented as apersonal computer including both laptop computer and non-laptop computerconfigurations. Additionally, the computing device 500 may includespecialized hardware such as an application-specific integrated circuit(ASIC), a field programmable gate array (FPGA), a programmable logicdevice (PLD), and/or a free form logic on an integrated circuit (IC),among others.

Example embodiments may also include methods to enhance and present amedical image using a corrective mechanism. These methods can beimplemented in any number of ways, including the structures describedherein. One such way may be by machine operations, of devices of thetype described in the present disclosure. Another optional way may befor one or more of the individual operations of the methods to beperformed in conjunction with one or more human operators performingsome of the operations while other operations may be performed bymachines. These human operators need not be collocated with each other,but each can be only with a machine that performs a portion of theprogram. In other embodiments, the human interaction can be automatedsuch as by pre-selected criteria that may be machine automated.

FIG. 6 is a logic flow diagram illustrating a process for enhancing andpresenting a medical image using a corrective mechanism. Process 600 maybe implemented on a computing device, such as the computing device 500or another system.

Process 600 begins with operation 610, where an image analysisapplication may capture a digital copy of the medical image displayed ona display device. The medical image may include an x-ray, a CT, anultrasound, and/or a PET scan. At operation 620, a flawed photographyeffect associated with the digital copy may be identified by processingthe digital copy. Next, at operation 630, the digital copy may beenhanced based on the flawed photography effect.

Furthermore, at operation 640, the enhanced digital copy may beprocessed with an artificial intelligence mechanism to generate anannotation. The annotation may designate a suspicious, a not suspicious,or a follow up label associated with a cancer identification. Atoperation 650, the enhanced digital copy and the annotation may bedisplayed.

The operations included in process 600 is for illustration purposes.Enhancing and presenting a medical image using a corrective mechanismmay be implemented by similar processes with fewer or additional steps,as well as in different order of operations using the principlesdescribed herein. The operations described herein may be executed by oneor more processors operated on one or more computing devices, one ormore processor cores, specialized processing devices, and/or specialpurpose processors, among other examples.

A method of enhancing and presenting a medical image using a correctivemechanism is also described. The method includes receiving a digitalcopy of the medical image from an image source. Next, a flawedphotography effect associated with the digital copy may be identified byprocessing the digital copy. The digital copy may be enhanced based onthe flawed photography effect. Furthermore, the digital copy may beprocessed with an artificial intelligence mechanism to generate anannotation. The annotation may include a suspicious label, a notsuspicious label, and/or a follow-up label associated with a canceridentification.

The digital copy and the annotation may next be provided for apresentation. When introducing elements of the present disclosure or theembodiment(s) thereof, the articles “a,” “an,” and “the” are intended tomean that there are one or more of the elements. Similarly, theadjective “another,” when used to introduce an element, is intended tomean one or more elements. The terms “including” and “having” areintended to be inclusive such that there may be additional elementsother than the listed elements.

Although this invention has been described with a certain degree ofparticularity, it is to be understood that the present disclosure hasbeen made only by way of illustration and that numerous changes in thedetails of construction and arrangement of parts may be resorted towithout departing from the spirit and the scope of the invention.

1-19. (canceled)
 20. A method comprising: receiving, by at least oneprocessor, a first digital medical image; identifying, by at least oneprocessor, a flawed photography effect associated with the first digitalmedical image by processing the first digital medical image using a deeplearning model; enhancing, by at least one processor, the first digitalmedical image based on the flawed photography effect using the deeplearning model; processing, by at least one processor, the first digitalmedical image using the deep learning model to generate an annotation,wherein the annotation includes at least one of a suspicious label, anot suspicious label, or a follow-up label associated with a canceridentification; and displaying, by at least one processor, the firstdigital medical image and the annotation overlaid on the first digitalmedical image.
 21. The method of claim 20, wherein the flawedphotography effect comprises at least one error resulting from a captureor production of the first digital medical image.
 22. The method ofclaim 21, wherein the error comprises an orientation, image blur, ared-eye detection, a blur, a color balance, an exposure, a noiseinformation, or a combination thereof.
 23. The method of claim 20,wherein the deep learning model comprises a classifier machine learningmodel trained to identify cancerous tissue represented in digitalimagery.
 24. The method of claim 20, wherein the suspicious labelrepresents an identification. of a malignant cancer.
 25. The method ofclaim 20, wherein the not suspicious label comprises represents anidentification of a benign cancer.
 26. The method of claim 20, whereinthe follow-up label comprises represents an identification of apotentially malignant cancer.
 27. A method comprising: receiving, by atleast one processor, a first digital medical image; identifying, by atleast one processor, a flawed photography effect associated with thefirst digital medical image by processing the first digital medicalimage using a deep learning model; enhancing, by at least one processor,the first digital medical image based on the flawed photography effectusing the deep learning model; processing, by at least one processor,the first digital medical image using the deep learning model togenerate an annotation, wherein the annotation includes at least oneclassification of a region-of-interest (ROI) within the first digitalmedical image; and displaying, by at least one processor, the firstdigital medical image and the annotation overlaid on the first digitalmedical image.
 28. The method of claim 27, wherein the flawedphotography effect comprises at least one error resulting from a captureor production of the first digital medical image.
 29. The method ofclaim 28, wherein the error comprises an orientation, image blur, ared-eye detection, a blur, a color balance, an exposure, a noiseinformation, or a combination thereof.
 30. The method of claim 27,wherein the deep learning model comprises a classifier machine learningmodel trained to identify cancerous tissue represented in digitalimagery.
 31. The method of claim 27, wherein the suspicious labelrepresents an identification of a malignant cancer.
 32. The method ofclaim 27, wherein the not suspicious label represents an identificationof a benign cancer.
 33. The method of claim 27, wherein the follow-uplabel represents an identification of a potentially malignant cancer.34. A system comprising: at least one processor configured to executeinstructions stored in a non-transitory storage medium, wherein theinstructions cause the at least one processor to perform steps to:receive a first digital medical image; identify a flawed photographyeffect associated with the first digital medical image by processing thefirst digital medical image using a deep learning model; enhance thefirst digital medical image based on the flawed photography effect usingthe deep learning model; process the first digital medical image usingthe deep learning model to generate an annotation, wherein theannotation includes at least one of a suspicious label, a not suspiciouslabel, or a follow-up label associated with a cancer identification; anddisplay the first digital medical image and the annotation overlaid onthe first digital medical image.
 35. The system of claim 34, wherein theflawed photography effect comprises at least one error resulting from acapture or production of the first digital medical image.
 36. The systemof claim 34, wherein the deep learning model comprises a classifiermachine learning model trained to identify cancerous tissue representedin digital imagery.
 37. The system of claim 34, wherein the suspiciouslabel represents an identification of a malignant cancer.
 38. The systemof claim 34, wherein the not suspicious label represents anidentification of a benign cancer.
 39. The system of claim 34, whereinthe follow-up label represents an identification of a potentiallymalignant cancer.